Climate, Weather, Socio-economic and Electricity Usage Data for The Residential and Commercial Sectors in FL, U.S

This paper presents the data that is used in the article entitled “Climate sensitivity of end-use electricity consumption in the built environment: An application to the state of Florida, United States” (Mukhopadhyay and Nateghi, 2017) [1]. The data described in this paper pertains to the state of Florida (during the period of January 1990 to November 2015). It can be classified into four categories of (i) state-level electricity consumption data; (ii) climate data; (iii) weather data; and (iv) socio-economic data. While, electricity consumption data and climate data are obtained at monthly scale directly from the source, the weather data was initially obtained at daily-level, and then aggregated to monthly level for the purpose of analysis. The time scale of socio-economic data varies from monthly-level to yearly-level. This dataset can be used to analyze the influence of climate and weather on the electricity demand as described in Mukhopadhyay and Nateghi (2017

A multi-paradigm framework to assess the impacts of climate change on end-use energy demand

Projecting the long-term trends in energy demand is an increasingly complex endeavor due to the uncertain emerging changes in factors such as climate and policy. The existing energy-economy paradigms used to characterize the long-term trends in the energy sector do not adequately account for climate variability and change. In this paper, we propose a multi-paradigm framework for estimating the climate sensitivity of end-use energy demand that can easily be integrated with the existing energy-economy models. To illustrate the applicability of our proposed framework, we used the energy demand and climate data in the state of Indiana to train a Bayesian predictive model. We then leveraged the end-use demand trends as well as downscaled future climate scenarios to generate probabilistic estimates of the future end-use demand for space cooling, space heating and water heating, at the individual household and building level, in the residential and commercial sectors. Our results indicated that the residential load is much more sensitive to climate variability and change than the commercial load. Moreover, since the largest fraction of the residential energy demand in Indiana is attributed to heating, future warming scenarios could lead to reduced end-use demand due to lower space heating and water heating needs. In the commercial sector, the overall energy demand is expected to increase under the future warming scenarios. This is because the increased cooling load during hotter summer months will likely outpace the reduced heating load during the more temperate winter months

Data On Major Power Outage Events in The Continental U.S.

This paper presents the data that is used in the article entitled “A Multi-Hazard Approach to Assess Severe Weather-Induced Major Power Outage Risks in the U.S.” (Mukherjee et al., 2018) [1]. The data described in this article pertains to the major outages witnessed by different states in the continental U.S. during January 2000–July 2016. As defined by the Department of Energy, the major outages refer to those that impacted atleast 50,000 custo- mers or caused an unplanned firm load loss of atleast 300 MW. Besides major outage data, this article also presents data on geo- graphical location of the outages, date and time of the outages, regional climatic information, land-use characteristics, electricity consumption patterns and economic characteristics of the states affected by the outages. This dataset can be used to identify and analyze the historical trends and patterns of the major outages and identify and assess the risk predictors associated with sustained power outages in the continental U.S. as described in Mukherjee et al. [1]

A Novel Methodological Approach to Estimate the Impact of Natural Hazard-Induced Disasters on Country/Region-Level Economic Growth

Abstract With the increased frequency of extreme weather events and large-scale disasters, extensive societal and economic losses incur every year due to damage of infrastructure and private properties, business disruptions, fatalities, homelessness, and severe health-related issues. In this article, we analyze the economic and disaster data from 1970 through 2010 to investigate the impact of disasters on country/region-level economic growth. We leveraged a random parameter modeling approach to develop the growth-econometrics model that identifies risk factors significantly influencing the country/region-level economic growth in the face of natural hazard-induced disasters, while controlling for country/region- and time-specific unobserved heterogeneities. We found that disaster intensity in terms of fatalities and homelessness, and economic characteristics such as openness to trade and a government’s consumption share of purchasing power parity (PPP), are the significant risk factors that randomly vary for different countries/regions. Other significant factors found to be significant include population, real gross domestic product (GDP), and investment share of PPP converted GDP per capita. We also found that flood is the most devastating disaster to affect country/region-level economic growth. This growth-econometrics model will help in the policy and decision making of governments related to the investment needs for pre- and post-disaster risk mitigation and response planning strategies, to better protect nations and minimize disaster-induced economic impacts

A study on alcohol dependence and it’s clinical implications among male patients attending a tertiary care hospital, Kolkata

Background: Alcohol intake is one of the major substance abuses and the problem of alcoholism is present throughout the world. It has been observed that alcohol dependence is associated with development of metabolic syndromes and chronic illnesses. Aims & Objectives: This study was conducted to find out the pattern of alcohol dependence and clinical features associated with different levels of alcohol dependence. Materials & Methods: It was a hospital based cross-sectional & observational study. Screening & assessment of alcohol dependence was done by using scales like AUDIT & ADS. Semi-structured questioner was used for assessing sociodemographic data for the total sample of 100 patients. 65 cases were selected finally after screening by AUDIT scale. Descriptive statistical analysis was done for obtaining results. Results: Findings suggests that among the patients a significant number of cases (41.5%) were in the category of substantial level of alcohol dependence. This study findings suggest that severity of alcohol dependence is directly associated with development of delirium ,physical comorbidities and sexual dysfunction.Conclusion: The burden of alcohol use is growing alarmingly in India and before it turns out to be a giant monster encroaching upon youths, we need to focus our attention in curbing this problem by diagnosing and treating alcohol dependent patients and using preventive measures, to not allow development of dependence at all

Projected end-use demand under RCP 4.5 and RCP 8.5 in the commercial sector (arranged in descending order of demands).

<p>Projected end-use demand under RCP 4.5 and RCP 8.5 in the commercial sector (arranged in descending order of demands).</p

Plot of fitted versus observed values of total end-use consumption (trillion Btu) in the residential sector.

<p>Plot of fitted versus observed values of total end-use consumption (trillion Btu) in the residential sector.</p

Predictive performance: The residential sector.

<p>Predictive performance: The residential sector.</p

Projected end-use demand under RCP 4.5 and RCP 8.5 in the commercial sector (arranged in descending order of demands).

<p>Projected end-use demand under RCP 4.5 and RCP 8.5 in the commercial sector (arranged in descending order of demands).</p